1. Field of the Invention
The present invention generally relates to computer assisted manufacturing processes and, more particularly, to a model for re-engineering a build-to-stock operation to a configure-to-order operation centered around “building blocks”, thereby keeping inventory only at the component level.
2. Background Description
A configure-to-order (CTO) system is a hybrid of make-to-stock and make-to-order operations: a set of components (subassemblies) are built to stock whereas the end products are assembled to order. This hybrid model is most suitable in an environment where the time it takes to assemble the end product is negligible, while the production/replenishment leadtime for each component is much more substantial. Personal Computer (PC) manufacturing is a good example of such an environment. By keeping inventory at the component level, customer orders can be filled quickly. On the other hand, postponing the final assembly until order arrival provides a high level of flexibility in terms of product variety, and also achieves resource pooling in terms of maximizing the usage of component inventory. Therefore, the CTO system appears to be an ideal business process model that provides both mass customization and a quick response time to order fulfillment.
Such a hybrid model is often referred to as an assemble-to-order (ATO) system in the research literature. In an ATO system, usually there is a pre-fixed set of end-product types from which customers must choose. In contrast, a CTO system takes the ATO concept one step further, in allowing each customer to configure his/her own product in terms of selecting a personalized set of components that go into the product. Aside from checking that the product so configured must “make sense”, there is no “menu” of product types that limits the customer's choice.
PC manufacturing traditionally has been a build-to-plan (or build-to-forecast) process, a process that is sometimes referred to as the “machine-type model” (MTM) operation. There is a set of end products, or MTMs. Demand forecasts over a future planning horizon are generated for each MTM, and updated periodically for each planning cycle, typically, a weekly cycle. A “materials requirements planning” (MRP) type explosion technique is then used to determine the requirements for the components over the planning horizon, based on the bill-of-materials (BOM) structure of each end product. Because of the random variation involved in demand forecasts, safety stock is usually kept for each end product, as well as at each component level, in order to meet a desirable customer service level. However, holding finished goods inventory for any length of time is very costly in the PC business, where product life cycle is measured in months and price reduction takes place almost every other week.
Y. Wang, “Service Levels in Production-Inventory Networks: Bottlenecks, Tradeoffs, and Optimization”, Ph.D. Dissertation, Columbia University, 1988, applies an asymptotic result in an optimization problem to minimize average inventory holding cost with a constraint on the order fill-rate. J. M. Swaminathan and S. R. Tayur, “Stochastic Programming Models for Managing Product Variety”, in Quantitative Models for Supply Chain Management, S. Tayur, R. Ganeshan and M. Magazine (eds.), Kluwer Academic Publishers, Norwell, 1999, pp. 585-624, use stochastic programming models to study three different strategies at the assembly stage; utilizing component commonality, postponement (the “vanilla box approach”), and integrating assembly task design and operations. Other related recent works, not necessarily in the CTO setting, include Y. Aviv and A. Federgruen, “The Benefits of Design for Postponement”, in Quantitative Models for Supply Chain Management, S. Tayur, R. Ganeshan and M. Magazine (eds.), Kluwer Academic Publishers, Norwell, 1999, pp. 553-584, A. Garg and H. L. Lee, “Managing Product Variety: An Operations Perspective”, in Quantitative Models for Supply Chain Management, S. Tayur, R. Ganeshan and M. Magazine (eds.), Kluwer Academic Publishers, Norwell, 1999, 467-490, L. Li, “The Role of Inventory in Delivery-Time Competition”, Management Science, 38 (1992), 182-197, and S. Mahajan and G. J. van Ryzin, “Retail Inventories and Consumer Choice”, in Quantitative Models for Supply Chain Management, S. Tayur, R. Ganeshan and M. Magazine (eds.), Kluwer Academic Publishers, Norwell, 1999, 491-552.